Overview

Dataset statistics

Number of variables20
Number of observations17858
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory97.2 B

Variable types

Numeric16
Categorical4

Warnings

prom_bill_amt is highly correlated with pay_amt1 and 5 other fieldsHigh correlation
pay_amt1 is highly correlated with prom_bill_amtHigh correlation
pay_amt2 is highly correlated with prom_bill_amtHigh correlation
pay_amt3 is highly correlated with prom_bill_amt and 2 other fieldsHigh correlation
pay_amt4 is highly correlated with prom_bill_amt and 2 other fieldsHigh correlation
pay_amt5 is highly correlated with prom_bill_amt and 3 other fieldsHigh correlation
pay_amt6 is highly correlated with prom_bill_amt and 3 other fieldsHigh correlation
prom_bill_amt is highly correlated with pay_amt1 and 5 other fieldsHigh correlation
pay_amt1 is highly correlated with prom_bill_amt and 1 other fieldsHigh correlation
pay_amt2 is highly correlated with prom_bill_amt and 2 other fieldsHigh correlation
pay_amt3 is highly correlated with prom_bill_amt and 4 other fieldsHigh correlation
pay_amt4 is highly correlated with prom_bill_amt and 4 other fieldsHigh correlation
pay_amt5 is highly correlated with prom_bill_amt and 4 other fieldsHigh correlation
pay_amt6 is highly correlated with prom_bill_amt and 3 other fieldsHigh correlation
df_index is highly correlated with default.payment.next.monthHigh correlation
limit_bal is highly correlated with default.payment.next.monthHigh correlation
age is highly correlated with default.payment.next.monthHigh correlation
prom_bill_amt is highly correlated with default.payment.next.monthHigh correlation
pay_amt1 is highly correlated with default.payment.next.monthHigh correlation
pay_amt2 is highly correlated with default.payment.next.monthHigh correlation
pay_amt3 is highly correlated with default.payment.next.monthHigh correlation
pay_amt4 is highly correlated with default.payment.next.monthHigh correlation
pay_amt5 is highly correlated with default.payment.next.monthHigh correlation
pay_amt6 is highly correlated with default.payment.next.monthHigh correlation
default.payment.next.month is highly correlated with df_index and 9 other fieldsHigh correlation
pay_amt1 is highly correlated with pay_amt2 and 7 other fieldsHigh correlation
pay_4 is highly correlated with pay_2 and 5 other fieldsHigh correlation
marriage is highly correlated with ageHigh correlation
pay_amt2 is highly correlated with pay_amt1 and 6 other fieldsHigh correlation
default.payment.next.month is highly correlated with pay_1High correlation
pay_amt4 is highly correlated with pay_amt1 and 5 other fieldsHigh correlation
pay_amt3 is highly correlated with pay_amt1 and 5 other fieldsHigh correlation
pay_2 is highly correlated with pay_amt1 and 7 other fieldsHigh correlation
pay_amt6 is highly correlated with pay_amt1 and 5 other fieldsHigh correlation
pay_5 is highly correlated with pay_4 and 5 other fieldsHigh correlation
pay_1 is highly correlated with pay_amt1 and 6 other fieldsHigh correlation
pay_6 is highly correlated with pay_4 and 5 other fieldsHigh correlation
age is highly correlated with marriageHigh correlation
prom_bill_amt is highly correlated with pay_amt1 and 10 other fieldsHigh correlation
pay_3 is highly correlated with pay_4 and 5 other fieldsHigh correlation
pay_amt5 is highly correlated with pay_amt1 and 5 other fieldsHigh correlation
df_index has unique values Unique
pay_1 has 8707 (48.8%) zeros Zeros
pay_2 has 9437 (52.8%) zeros Zeros
pay_3 has 9403 (52.7%) zeros Zeros
pay_4 has 9663 (54.1%) zeros Zeros
pay_5 has 9870 (55.3%) zeros Zeros
pay_6 has 9481 (53.1%) zeros Zeros
prom_bill_amt has 834 (4.7%) zeros Zeros
pay_amt1 has 3982 (22.3%) zeros Zeros
pay_amt2 has 4180 (23.4%) zeros Zeros
pay_amt3 has 4575 (25.6%) zeros Zeros
pay_amt4 has 4923 (27.6%) zeros Zeros
pay_amt5 has 5176 (29.0%) zeros Zeros
pay_amt6 has 5575 (31.2%) zeros Zeros

Reproduction

Analysis started2021-09-30 00:57:13.007260
Analysis finished2021-09-30 00:57:59.290023
Duration46.28 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct17858
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14848.82305
Minimum0
Maximum29999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size139.6 KiB
2021-09-29T18:57:59.386024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1466.85
Q17412.25
median14872.5
Q322105.75
95-th percentile28481.15
Maximum29999
Range29999
Interquartile range (IQR)14693.5

Descriptive statistics

Standard deviation8614.799685
Coefficient of variation (CV)0.5801671726
Kurtosis-1.1759603
Mean14848.82305
Median Absolute Deviation (MAD)7354.5
Skewness0.02364426411
Sum265170282
Variance74214773.61
MonotonicityStrictly increasing
2021-09-29T18:57:59.514024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
196011
 
< 0.1%
196121
 
< 0.1%
196071
 
< 0.1%
196061
 
< 0.1%
196041
 
< 0.1%
196031
 
< 0.1%
196021
 
< 0.1%
195991
 
< 0.1%
195731
 
< 0.1%
Other values (17848)17848
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
51
< 0.1%
71
< 0.1%
81
< 0.1%
101
< 0.1%
131
< 0.1%
141
< 0.1%
ValueCountFrequency (%)
299991
< 0.1%
299921
< 0.1%
299911
< 0.1%
299901
< 0.1%
299891
< 0.1%
299861
< 0.1%
299851
< 0.1%
299841
< 0.1%
299821
< 0.1%
299811
< 0.1%

limit_bal
Real number (ℝ≥0)

HIGH CORRELATION

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127132.4896
Minimum10000
Maximum520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size139.6 KiB
2021-09-29T18:57:59.651024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median90000
Q3180000
95-th percentile360000
Maximum520000
Range510000
Interquartile range (IQR)130000

Descriptive statistics

Standard deviation106425.0419
Coefficient of variation (CV)0.8371191522
Kurtosis1.141738625
Mean127132.4896
Median Absolute Deviation (MAD)60000
Skewness1.247992433
Sum2270332000
Variance1.132628955 × 1010
MonotonicityNot monotonic
2021-09-29T18:57:59.796024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500002686
 
15.0%
200001665
 
9.3%
300001356
 
7.6%
800001118
 
6.3%
100000739
 
4.1%
200000699
 
3.9%
60000610
 
3.4%
70000571
 
3.2%
150000562
 
3.1%
180000535
 
3.0%
Other values (43)7317
41.0%
ValueCountFrequency (%)
10000442
 
2.5%
160002
 
< 0.1%
200001665
9.3%
300001356
7.6%
40000189
 
1.1%
500002686
15.0%
60000610
 
3.4%
70000571
 
3.2%
800001118
6.3%
90000449
 
2.5%
ValueCountFrequency (%)
5200003
 
< 0.1%
5100004
 
< 0.1%
500000153
0.9%
4900009
 
0.1%
48000014
 
0.1%
47000019
 
0.1%
46000017
 
0.1%
45000063
0.4%
44000015
 
0.1%
43000021
 
0.1%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.7 KiB
2
10721 
1
7137 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
210721
60.0%
17137
40.0%

Length

2021-09-29T18:58:00.023024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-29T18:58:00.086023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
210721
60.0%
17137
40.0%

Most occurring characters

ValueCountFrequency (%)
210721
60.0%
17137
40.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
210721
60.0%
17137
40.0%

Most occurring scripts

ValueCountFrequency (%)
Common17858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
210721
60.0%
17137
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
210721
60.0%
17137
40.0%

education
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
2
8842 
1
5530 
3
3245 
5
 
179
4
 
62

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17858
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
28842
49.5%
15530
31.0%
33245
 
18.2%
5179
 
1.0%
462
 
0.3%

Length

2021-09-29T18:58:00.252024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-29T18:58:00.318024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
28842
49.5%
15530
31.0%
33245
 
18.2%
5179
 
1.0%
462
 
0.3%

Most occurring characters

ValueCountFrequency (%)
28842
49.5%
15530
31.0%
33245
 
18.2%
5179
 
1.0%
462
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
28842
49.5%
15530
31.0%
33245
 
18.2%
5179
 
1.0%
462
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common17858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
28842
49.5%
15530
31.0%
33245
 
18.2%
5179
 
1.0%
462
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII17858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28842
49.5%
15530
31.0%
33245
 
18.2%
5179
 
1.0%
462
 
0.3%

marriage
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.7 KiB
2
9586 
1
8014 
3
 
258

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17858
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
29586
53.7%
18014
44.9%
3258
 
1.4%

Length

2021-09-29T18:58:00.497024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-29T18:58:00.560024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
29586
53.7%
18014
44.9%
3258
 
1.4%

Most occurring characters

ValueCountFrequency (%)
29586
53.7%
18014
44.9%
3258
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
29586
53.7%
18014
44.9%
3258
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common17858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
29586
53.7%
18014
44.9%
3258
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII17858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29586
53.7%
18014
44.9%
3258
 
1.4%

age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.02391085
Minimum21
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size139.6 KiB
2021-09-29T18:58:00.641024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q127
median33
Q341
95-th percentile53
Maximum60
Range39
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.195665975
Coefficient of variation (CV)0.2625539453
Kurtosis-0.5065206304
Mean35.02391085
Median Absolute Deviation (MAD)7
Skewness0.6024361118
Sum625457
Variance84.56027273
MonotonicityNot monotonic
2021-09-29T18:58:00.758024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
29920
 
5.2%
27910
 
5.1%
25821
 
4.6%
24818
 
4.6%
26808
 
4.5%
28798
 
4.5%
30759
 
4.3%
23691
 
3.9%
31680
 
3.8%
34641
 
3.6%
Other values (30)10012
56.1%
ValueCountFrequency (%)
2158
 
0.3%
22460
2.6%
23691
3.9%
24818
4.6%
25821
4.6%
26808
4.5%
27910
5.1%
28798
4.5%
29920
5.2%
30759
4.3%
ValueCountFrequency (%)
6052
 
0.3%
5963
 
0.4%
5884
 
0.5%
5779
 
0.4%
56131
0.7%
55137
0.8%
54165
0.9%
53204
1.1%
52203
1.1%
51216
1.2%

pay_1
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1180983313
Minimum-2
Maximum8
Zeros8707
Zeros (%)48.8%
Negative4213
Negative (%)23.6%
Memory size139.6 KiB
2021-09-29T18:58:00.863024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q10
median0
Q31
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.156992145
Coefficient of variation (CV)9.796854304
Kurtosis2.536265681
Mean0.1180983313
Median Absolute Deviation (MAD)1
Skewness0.6813061545
Sum2109
Variance1.338630824
MonotonicityNot monotonic
2021-09-29T18:58:00.949024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
08707
48.8%
-12740
 
15.3%
12662
 
14.9%
21905
 
10.7%
-21473
 
8.2%
3268
 
1.5%
457
 
0.3%
518
 
0.1%
814
 
0.1%
69
 
0.1%
ValueCountFrequency (%)
-21473
 
8.2%
-12740
 
15.3%
08707
48.8%
12662
 
14.9%
21905
 
10.7%
3268
 
1.5%
457
 
0.3%
518
 
0.1%
69
 
0.1%
75
 
< 0.1%
ValueCountFrequency (%)
814
 
0.1%
75
 
< 0.1%
69
 
0.1%
518
 
0.1%
457
 
0.3%
3268
 
1.5%
21905
 
10.7%
12662
 
14.9%
08707
48.8%
-12740
 
15.3%

pay_2
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01926307537
Minimum-2
Maximum8
Zeros9437
Zeros (%)52.8%
Negative5139
Negative (%)28.8%
Memory size139.6 KiB
2021-09-29T18:58:01.033024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.272506923
Coefficient of variation (CV)-66.05938558
Kurtosis1.098353621
Mean-0.01926307537
Median Absolute Deviation (MAD)0
Skewness0.680905606
Sum-344
Variance1.61927387
MonotonicityNot monotonic
2021-09-29T18:58:01.122025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
09437
52.8%
22893
 
16.2%
-12791
 
15.6%
-22348
 
13.1%
3255
 
1.4%
482
 
0.5%
520
 
0.1%
715
 
0.1%
19
 
0.1%
67
 
< 0.1%
ValueCountFrequency (%)
-22348
 
13.1%
-12791
 
15.6%
09437
52.8%
19
 
0.1%
22893
 
16.2%
3255
 
1.4%
482
 
0.5%
520
 
0.1%
67
 
< 0.1%
715
 
0.1%
ValueCountFrequency (%)
81
 
< 0.1%
715
 
0.1%
67
 
< 0.1%
520
 
0.1%
482
 
0.5%
3255
 
1.4%
22893
 
16.2%
19
 
0.1%
09437
52.8%
-12791
 
15.6%

pay_3
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.06837271811
Minimum-2
Maximum8
Zeros9403
Zeros (%)52.7%
Negative5339
Negative (%)29.9%
Memory size139.6 KiB
2021-09-29T18:58:01.208024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.288646667
Coefficient of variation (CV)-18.84738098
Kurtosis1.705159725
Mean-0.06837271811
Median Absolute Deviation (MAD)0
Skewness0.761167977
Sum-1221
Variance1.660610232
MonotonicityNot monotonic
2021-09-29T18:58:01.291025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
09403
52.7%
22798
 
15.7%
-22688
 
15.1%
-12651
 
14.8%
3198
 
1.1%
460
 
0.3%
726
 
0.1%
618
 
0.1%
514
 
0.1%
82
 
< 0.1%
ValueCountFrequency (%)
-22688
 
15.1%
-12651
 
14.8%
09403
52.7%
22798
 
15.7%
3198
 
1.1%
460
 
0.3%
514
 
0.1%
618
 
0.1%
726
 
0.1%
82
 
< 0.1%
ValueCountFrequency (%)
82
 
< 0.1%
726
 
0.1%
618
 
0.1%
514
 
0.1%
460
 
0.3%
3198
 
1.1%
22798
 
15.7%
09403
52.7%
-12651
 
14.8%
-22688
 
15.1%

pay_4
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1504087804
Minimum-2
Maximum8
Zeros9663
Zeros (%)54.1%
Negative5578
Negative (%)31.2%
Memory size139.6 KiB
2021-09-29T18:58:01.370023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.279630108
Coefficient of variation (CV)-8.507682228
Kurtosis3.097011255
Mean-0.1504087804
Median Absolute Deviation (MAD)0
Skewness0.9839738753
Sum-2686
Variance1.637453213
MonotonicityNot monotonic
2021-09-29T18:58:01.464026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
09663
54.1%
-22980
 
16.7%
-12598
 
14.5%
22329
 
13.0%
3145
 
0.8%
756
 
0.3%
454
 
0.3%
529
 
0.2%
63
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
-22980
 
16.7%
-12598
 
14.5%
09663
54.1%
22329
 
13.0%
3145
 
0.8%
454
 
0.3%
529
 
0.2%
63
 
< 0.1%
756
 
0.3%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
756
 
0.3%
63
 
< 0.1%
529
 
0.2%
454
 
0.3%
3145
 
0.8%
22329
 
13.0%
09663
54.1%
-12598
 
14.5%
-22980
 
16.7%

pay_5
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.220853399
Minimum-2
Maximum8
Zeros9870
Zeros (%)55.3%
Negative5778
Negative (%)32.4%
Memory size139.6 KiB
2021-09-29T18:58:01.554047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.247385218
Coefficient of variation (CV)-5.648023638
Kurtosis3.592689755
Mean-0.220853399
Median Absolute Deviation (MAD)0
Skewness1.030325462
Sum-3944
Variance1.555969883
MonotonicityNot monotonic
2021-09-29T18:58:01.638047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
09870
55.3%
-23199
 
17.9%
-12579
 
14.4%
21924
 
10.8%
3146
 
0.8%
469
 
0.4%
755
 
0.3%
512
 
0.1%
63
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
-23199
 
17.9%
-12579
 
14.4%
09870
55.3%
21924
 
10.8%
3146
 
0.8%
469
 
0.4%
512
 
0.1%
63
 
< 0.1%
755
 
0.3%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
755
 
0.3%
63
 
< 0.1%
512
 
0.1%
469
 
0.4%
3146
 
0.8%
21924
 
10.8%
09870
55.3%
-12579
 
14.4%
-23199
 
17.9%

pay_6
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2645872998
Minimum-2
Maximum8
Zeros9481
Zeros (%)53.1%
Negative6169
Negative (%)34.5%
Memory size139.6 KiB
2021-09-29T18:58:01.717025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.258021805
Coefficient of variation (CV)-4.754656802
Kurtosis3.100133647
Mean-0.2645872998
Median Absolute Deviation (MAD)0
Skewness0.9638072866
Sum-4725
Variance1.582618861
MonotonicityNot monotonic
2021-09-29T18:58:01.810024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
09481
53.1%
-23518
 
19.7%
-12651
 
14.8%
21944
 
10.9%
3157
 
0.9%
746
 
0.3%
439
 
0.2%
612
 
0.1%
59
 
0.1%
81
 
< 0.1%
ValueCountFrequency (%)
-23518
 
19.7%
-12651
 
14.8%
09481
53.1%
21944
 
10.9%
3157
 
0.9%
439
 
0.2%
59
 
0.1%
612
 
0.1%
746
 
0.3%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
746
 
0.3%
612
 
0.1%
59
 
0.1%
439
 
0.2%
3157
 
0.9%
21944
 
10.9%
09481
53.1%
-12651
 
14.8%
-23518
 
19.7%

prom_bill_amt
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15803
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26808.36984
Minimum-43253.83333
Maximum134098.5
Zeros834
Zeros (%)4.7%
Negative162
Negative (%)0.9%
Memory size139.6 KiB
2021-09-29T18:58:01.918023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-43253.83333
5-th percentile0
Q11847.875
median17242.91667
Q340623.29167
95-th percentile93056.875
Maximum134098.5
Range177352.3333
Interquartile range (IQR)38775.41667

Descriptive statistics

Standard deviation30282.80622
Coefficient of variation (CV)1.129602673
Kurtosis1.30972247
Mean26808.36984
Median Absolute Deviation (MAD)16233.83333
Skewness1.369884113
Sum478743868.7
Variance917048352.8
MonotonicityNot monotonic
2021-09-29T18:58:02.046024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0834
 
4.7%
39054
 
0.3%
416.666666731
 
0.2%
240029
 
0.2%
32525
 
0.1%
105021
 
0.1%
45521
 
0.1%
32620
 
0.1%
31618
 
0.1%
26018
 
0.1%
Other values (15793)16787
94.0%
ValueCountFrequency (%)
-43253.833331
< 0.1%
-132551
< 0.1%
-6467.8333331
< 0.1%
-5109.6666671
< 0.1%
-4913.3333331
< 0.1%
-48941
< 0.1%
-29971
< 0.1%
-2916.6666671
< 0.1%
-29001
< 0.1%
-2851.51
< 0.1%
ValueCountFrequency (%)
134098.51
< 0.1%
133989.51
< 0.1%
133779.33331
< 0.1%
133698.83331
< 0.1%
133576.51
< 0.1%
133223.51
< 0.1%
133218.51
< 0.1%
133048.16671
< 0.1%
132985.51
< 0.1%
132780.33331
< 0.1%

pay_amt1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3804
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1978.817617
Minimum0
Maximum9156
Zeros3982
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size139.6 KiB
2021-09-29T18:58:02.174024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1316
median1700
Q33000
95-th percentile5541.2
Maximum9156
Range9156
Interquartile range (IQR)2684

Descriptive statistics

Standard deviation1820.160726
Coefficient of variation (CV)0.9198223781
Kurtosis0.9693086281
Mean1978.817617
Median Absolute Deviation (MAD)1300
Skewness1.060384336
Sum35337725
Variance3312985.068
MonotonicityNot monotonic
2021-09-29T18:58:02.297023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03982
 
22.3%
20001135
 
6.4%
3000661
 
3.7%
1500445
 
2.5%
5000393
 
2.2%
4000286
 
1.6%
2500251
 
1.4%
1000245
 
1.4%
1300164
 
0.9%
390163
 
0.9%
Other values (3794)10133
56.7%
ValueCountFrequency (%)
03982
22.3%
17
 
< 0.1%
28
 
< 0.1%
310
 
0.1%
49
 
0.1%
53
 
< 0.1%
68
 
< 0.1%
74
 
< 0.1%
82
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
91561
 
< 0.1%
91481
 
< 0.1%
91171
 
< 0.1%
90541
 
< 0.1%
90521
 
< 0.1%
90481
 
< 0.1%
90261
 
< 0.1%
90041
 
< 0.1%
900017
0.1%
89921
 
< 0.1%

pay_amt2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3592
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1818.778979
Minimum0
Maximum8090
Zeros4180
Zeros (%)23.4%
Negative0
Negative (%)0.0%
Memory size139.6 KiB
2021-09-29T18:58:02.418024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1199.25
median1522
Q32750.75
95-th percentile5016
Maximum8090
Range8090
Interquartile range (IQR)2551.5

Descriptive statistics

Standard deviation1677.681371
Coefficient of variation (CV)0.9224217954
Kurtosis0.681896554
Mean1818.778979
Median Absolute Deviation (MAD)1278
Skewness0.9922290787
Sum32479755
Variance2814614.782
MonotonicityNot monotonic
2021-09-29T18:58:02.539024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04180
23.4%
20001077
 
6.0%
3000640
 
3.6%
1500465
 
2.6%
1000429
 
2.4%
5000364
 
2.0%
4000273
 
1.5%
2500213
 
1.2%
390197
 
1.1%
1600166
 
0.9%
Other values (3582)9854
55.2%
ValueCountFrequency (%)
04180
23.4%
112
 
0.1%
213
 
0.1%
311
 
0.1%
46
 
< 0.1%
514
 
0.1%
66
 
< 0.1%
78
 
< 0.1%
86
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
80901
< 0.1%
80891
< 0.1%
80801
< 0.1%
80631
< 0.1%
80391
< 0.1%
80171
< 0.1%
80161
< 0.1%
80041
< 0.1%
80031
< 0.1%
80021
< 0.1%

pay_amt3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3351
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1508.541606
Minimum0
Maximum7200
Zeros4575
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size139.6 KiB
2021-09-29T18:58:02.663024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1180
Q32183.5
95-th percentile4941
Maximum7200
Range7200
Interquartile range (IQR)2183.5

Descriptive statistics

Standard deviation1518.364686
Coefficient of variation (CV)1.00651164
Kurtosis0.9197424052
Mean1508.541606
Median Absolute Deviation (MAD)1120
Skewness1.140298933
Sum26939536
Variance2305431.319
MonotonicityNot monotonic
2021-09-29T18:58:02.781024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04575
25.6%
20001055
 
5.9%
1000887
 
5.0%
3000616
 
3.4%
1500420
 
2.4%
5000318
 
1.8%
4000223
 
1.2%
2500193
 
1.1%
1200189
 
1.1%
390175
 
1.0%
Other values (3341)9207
51.6%
ValueCountFrequency (%)
04575
25.6%
18
 
< 0.1%
210
 
0.1%
37
 
< 0.1%
47
 
< 0.1%
58
 
< 0.1%
69
 
0.1%
79
 
0.1%
84
 
< 0.1%
95
 
< 0.1%
ValueCountFrequency (%)
72003
< 0.1%
71781
 
< 0.1%
71711
 
< 0.1%
71691
 
< 0.1%
71641
 
< 0.1%
71581
 
< 0.1%
71421
 
< 0.1%
71221
 
< 0.1%
71005
< 0.1%
70691
 
< 0.1%

pay_amt4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3090
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1316.305353
Minimum0
Maximum6249
Zeros4923
Zeros (%)27.6%
Negative0
Negative (%)0.0%
Memory size139.6 KiB
2021-09-29T18:58:03.166045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1000
Q32000
95-th percentile4206
Maximum6249
Range6249
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation1400.574887
Coefficient of variation (CV)1.064019745
Kurtosis0.7451451057
Mean1316.305353
Median Absolute Deviation (MAD)1000
Skewness1.164695852
Sum23506581
Variance1961610.014
MonotonicityNot monotonic
2021-09-29T18:58:03.280024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04923
27.6%
10001152
 
6.5%
2000968
 
5.4%
3000634
 
3.6%
1500382
 
2.1%
5000322
 
1.8%
4000243
 
1.4%
2500210
 
1.2%
500209
 
1.2%
390179
 
1.0%
Other values (3080)8636
48.4%
ValueCountFrequency (%)
04923
27.6%
111
 
0.1%
214
 
0.1%
38
 
< 0.1%
411
 
0.1%
55
 
< 0.1%
65
 
< 0.1%
76
 
< 0.1%
83
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
62491
< 0.1%
62181
< 0.1%
62061
< 0.1%
62002
< 0.1%
61961
< 0.1%
61851
< 0.1%
61741
< 0.1%
61701
< 0.1%
61371
< 0.1%
61311
< 0.1%

pay_amt5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2982
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1292.885821
Minimum0
Maximum5990
Zeros5176
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size139.6 KiB
2021-09-29T18:58:03.401024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1000
Q32000
95-th percentile4129.05
Maximum5990
Range5990
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation1376.264676
Coefficient of variation (CV)1.064490501
Kurtosis0.547254358
Mean1292.885821
Median Absolute Deviation (MAD)1000
Skewness1.113872725
Sum23088355
Variance1894104.459
MonotonicityNot monotonic
2021-09-29T18:58:03.518046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05176
29.0%
10001116
 
6.2%
20001035
 
5.8%
3000653
 
3.7%
1500369
 
2.1%
5000362
 
2.0%
4000240
 
1.3%
500216
 
1.2%
2500190
 
1.1%
390152
 
0.9%
Other values (2972)8349
46.8%
ValueCountFrequency (%)
05176
29.0%
112
 
0.1%
26
 
< 0.1%
38
 
< 0.1%
43
 
< 0.1%
53
 
< 0.1%
64
 
< 0.1%
74
 
< 0.1%
83
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
59901
< 0.1%
59681
< 0.1%
59541
< 0.1%
59531
< 0.1%
59491
< 0.1%
59461
< 0.1%
59411
< 0.1%
59371
< 0.1%
59302
< 0.1%
59291
< 0.1%

pay_amt6
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2955
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1238.645145
Minimum0
Maximum5496
Zeros5575
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size139.6 KiB
2021-09-29T18:58:03.638023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median916.5
Q32000
95-th percentile4000
Maximum5496
Range5496
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation1349.44106
Coefficient of variation (CV)1.089449279
Kurtosis0.4464764644
Mean1238.645145
Median Absolute Deviation (MAD)916.5
Skewness1.102567373
Sum22119725
Variance1820991.173
MonotonicityNot monotonic
2021-09-29T18:58:03.755024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05575
31.2%
10001067
 
6.0%
20001021
 
5.7%
3000660
 
3.7%
1500388
 
2.2%
5000335
 
1.9%
4000235
 
1.3%
500218
 
1.2%
2500173
 
1.0%
390144
 
0.8%
Other values (2945)8042
45.0%
ValueCountFrequency (%)
05575
31.2%
115
 
0.1%
26
 
< 0.1%
35
 
< 0.1%
46
 
< 0.1%
54
 
< 0.1%
63
 
< 0.1%
72
 
< 0.1%
83
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
54961
< 0.1%
54951
< 0.1%
54851
< 0.1%
54831
< 0.1%
54541
< 0.1%
54302
< 0.1%
54191
< 0.1%
54111
< 0.1%
54101
< 0.1%
54071
< 0.1%

default.payment.next.month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.6 KiB
0
13154 
1
4704 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
013154
73.7%
14704
 
26.3%

Length

2021-09-29T18:58:03.948023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-29T18:58:04.008023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
013154
73.7%
14704
 
26.3%

Most occurring characters

ValueCountFrequency (%)
013154
73.7%
14704
 
26.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013154
73.7%
14704
 
26.3%

Most occurring scripts

ValueCountFrequency (%)
Common17858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
013154
73.7%
14704
 
26.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII17858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
013154
73.7%
14704
 
26.3%

Interactions

2021-09-29T18:57:30.789993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:30.927993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:31.056016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:31.188043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:31.308021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:31.438039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:31.568994image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:31.701015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:31.812016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:31.919015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:32.031017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:32.133017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:32.243042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:32.349016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:32.452016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-09-29T18:57:53.619023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:53.724023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:53.828022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:53.932023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:54.035023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:54.139024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:54.242022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:54.354024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:54.457023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:54.569023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:54.687024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:54.796023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:54.904023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:55.008024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:55.325023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:55.434024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:55.544024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:55.647024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:55.748023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:55.855028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:55.961025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:56.069026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:56.178027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:56.288046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:56.390053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:56.499079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:56.606053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:56.710079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:56.814079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:56.917053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:57.020053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:57.123023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:57.230078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:57.328073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:57.427052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:57.527047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:57.629066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:57.733049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:57.835024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:57.948023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:58.058023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:58.174023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:58.288075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:58.407027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-29T18:57:58.538024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-09-29T18:58:04.077024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-29T18:58:04.237023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-29T18:58:04.399024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-29T18:58:04.581024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-29T18:58:04.766024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-29T18:57:58.769022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-29T18:57:59.126024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexlimit_balsexeducationmarriageagepay_1pay_2pay_3pay_4pay_5pay_6prom_bill_amtpay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6default.payment.next.month
0020000.02212422-1-1-2-21284.0000000.0689.00.00.00.00.01
11120000.022226-1200022846.1666670.01000.01000.01000.00.02000.01
2290000.02223400000016942.1666671518.01500.01000.01000.01000.05000.00
3350000.02213700000038555.6666672000.02019.01200.01100.01069.01000.00
4550000.01123700000039685.6666672500.01815.0657.01000.01000.0800.00
57100000.0222230-1-100-12247.666667380.0601.00.0581.01687.01542.00
68140000.02312800200010868.6666673329.00.0432.01000.01000.01000.00
710200000.02323400200-15744.5000002306.012.050.0300.03738.066.00
81370000.01223012200256447.5000003200.00.03000.03000.01500.00.01
914250000.01122900000062265.1666673000.03000.03000.03000.03000.03000.00

Last rows

df_indexlimit_balsexeducationmarriageagepay_1pay_2pay_3pay_4pay_5pay_6prom_bill_amtpay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6default.payment.next.month
178482998150000.01214412220029009.8333332300.01700.00.0517.0503.0585.00
178492998290000.01213600000010810.5000001500.01500.01500.01200.02500.00.01
178502998430000.012238-1-1-2-1-1-11899.333333923.02977.01999.03057.03319.01000.00
1785129985240000.011230-2-2-2-2-2-20.0000000.00.00.00.00.00.00
1785229986360000.011235-1-1-2-2-2-2370.0000000.00.00.00.00.00.00
1785329989150000.011235-1-1-1-1-1-22201.8333339054.00.0783.00.00.00.00
1785429990140000.012141000000108105.8333336000.07000.04228.01505.02000.02000.00
1785529991210000.0121343222222500.0000000.00.00.00.00.00.01
178562999210000.013143000-2-2-23200.3333332000.00.00.00.00.00.00
178572999950000.01214600000038479.0000002078.01800.01430.01000.01000.01000.01